File size: 7,059 Bytes
6b31279 39b3b3e 7d66801 39b3b3e 6b31279 39b3b3e 6b31279 39b3b3e 6b31279 39b3b3e 6b31279 39b3b3e 6b31279 39b3b3e 6b31279 09d9587 39b3b3e 09d9587 39b3b3e 09d9587 39b3b3e 09d9587 39b3b3e 09d9587 39b3b3e f80684a 39b3b3e f80684a 39b3b3e f80684a 39b3b3e f80684a 39b3b3e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 |
# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang)
#
# See LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from huggingface_hub import hf_hub_download
from functools import lru_cache
from offline_asr import OfflineAsr
sample_rate = 16000
@lru_cache(maxsize=30)
def get_pretrained_model(repo_id: str) -> OfflineAsr:
if repo_id in chinese_models:
return chinese_models[repo_id](repo_id)
elif repo_id in english_models:
return english_models[repo_id](repo_id)
elif repo_id in chinese_english_mixed_models:
return chinese_english_mixed_models[repo_id](repo_id)
else:
raise ValueError(f"Unsupported repo_id: {repo_id}")
def _get_nn_model_filename(
repo_id: str,
filename: str,
subfolder: str = "exp",
) -> str:
nn_model_filename = hf_hub_download(
repo_id=repo_id,
filename=filename,
subfolder=subfolder,
)
return nn_model_filename
def _get_bpe_model_filename(
repo_id: str,
filename: str = "bpe.model",
subfolder: str = "data/lang_bpe_500",
) -> str:
bpe_model_filename = hf_hub_download(
repo_id=repo_id,
filename=filename,
subfolder=subfolder,
)
return bpe_model_filename
def _get_token_filename(
repo_id: str,
filename: str = "tokens.txt",
subfolder: str = "data/lang_char",
) -> str:
token_filename = hf_hub_download(
repo_id=repo_id,
filename=filename,
subfolder=subfolder,
)
return token_filename
@lru_cache(maxsize=10)
def _get_aishell2_pretrained_model(repo_id: str) -> OfflineAsr:
assert repo_id in [
# context-size 1
"yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12", # noqa
# context-size 2
"yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-B-2022-07-12", # noqa
]
nn_model_filename = _get_nn_model_filename(
repo_id=repo_id,
filename="cpu_jit.pt",
)
token_filename = _get_token_filename(repo_id=repo_id)
return OfflineAsr(
nn_model_filename=nn_model_filename,
bpe_model_filename=None,
token_filename=token_filename,
sample_rate=sample_rate,
device="cpu",
)
@lru_cache(maxsize=10)
def _get_gigaspeech_pre_trained_model(repo_id: str) -> OfflineAsr:
assert repo_id in [
"wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2",
]
nn_model_filename = _get_nn_model_filename(
# It is converted from https://huggingface.co/wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2 # noqa
repo_id="csukuangfj/icefall-asr-gigaspeech-pruned-transducer-stateless2", # noqa
filename="cpu_jit-epoch-29-avg-11-torch-1.10.0.pt",
)
bpe_model_filename = _get_bpe_model_filename(repo_id=repo_id)
return OfflineAsr(
nn_model_filename=nn_model_filename,
bpe_model_filename=bpe_model_filename,
token_filename=None,
sample_rate=sample_rate,
device="cpu",
)
@lru_cache(maxsize=10)
def _get_librispeech_pre_trained_model(repo_id: str) -> OfflineAsr:
assert repo_id in [
"csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13", # noqa
]
nn_model_filename = _get_nn_model_filename(
repo_id=repo_id,
filename="cpu_jit.pt",
)
bpe_model_filename = _get_bpe_model_filename(repo_id=repo_id)
return OfflineAsr(
nn_model_filename=nn_model_filename,
bpe_model_filename=bpe_model_filename,
token_filename=None,
sample_rate=sample_rate,
device="cpu",
)
@lru_cache(maxsize=10)
def _get_wenetspeech_pre_trained_model(repo_id: str):
assert repo_id in [
"luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2",
]
nn_model_filename = _get_nn_model_filename(
repo_id=repo_id,
filename="cpu_jit_epoch_10_avg_2_torch_1.7.1.pt",
)
token_filename = _get_token_filename(repo_id=repo_id)
return OfflineAsr(
nn_model_filename=nn_model_filename,
bpe_model_filename=None,
token_filename=token_filename,
sample_rate=sample_rate,
device="cpu",
)
@lru_cache(maxsize=10)
def _get_tal_csasr_pre_trained_model(repo_id: str):
assert repo_id in [
"luomingshuang/icefall_asr_tal-csasr_pruned_transducer_stateless5",
]
nn_model_filename = _get_nn_model_filename(
repo_id=repo_id,
filename="cpu_jit.pt",
)
token_filename = _get_token_filename(repo_id=repo_id)
return OfflineAsr(
nn_model_filename=nn_model_filename,
bpe_model_filename=None,
token_filename=token_filename,
sample_rate=sample_rate,
device="cpu",
)
@lru_cache(maxsize=10)
def _get_alimeeting_pre_trained_model(repo_id: str):
assert repo_id in [
"luomingshuang/icefall_asr_alimeeting_pruned_transducer_stateless2",
]
nn_model_filename = _get_nn_model_filename(
repo_id=repo_id,
filename="cpu_jit_torch_1.7.1.pt",
)
token_filename = _get_token_filename(repo_id=repo_id)
return OfflineAsr(
nn_model_filename=nn_model_filename,
bpe_model_filename=None,
token_filename=token_filename,
sample_rate=sample_rate,
device="cpu",
)
chinese_models = {
"luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2": _get_wenetspeech_pre_trained_model, # noqa
"yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-A-2022-07-12": _get_aishell2_pretrained_model, # noqa
"yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-B-2022-07-12": _get_aishell2_pretrained_model, # noqa
"luomingshuang/icefall_asr_alimeeting_pruned_transducer_stateless2": _get_alimeeting_pre_trained_model, # noqa
}
english_models = {
"wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2": _get_gigaspeech_pre_trained_model, # noqa
"csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13": _get_librispeech_pre_trained_model, # noqa
}
chinese_english_mixed_models = {
"luomingshuang/icefall_asr_tal-csasr_pruned_transducer_stateless5": _get_tal_csasr_pre_trained_model, # noqa
}
all_models = {
**chinese_models,
**english_models,
**chinese_english_mixed_models,
}
language_to_models = {
"Chinese": list(chinese_models.keys()),
"English": list(english_models.keys()),
"Chinese+English": list(chinese_english_mixed_models.keys()),
}
|